In this paper, we propose a practical approach to minimizing embedding impact in steganography based on syndrome
coding and trellis-coded quantization and contrast its performance with bounds derived from appropriate
rate-distortion bounds. We assume that each cover element can be assigned a positive scalar expressing the impact
of making an embedding change at that element (single-letter distortion). The problem is to embed a given
payload with minimal possible average embedding impact. This task, which can be viewed as a generalization of
matrix embedding or writing on wet paper, has been approached using heuristic and suboptimal tools in the past.
Here, we propose a fast and very versatile solution to this problem that can theoretically achieve performance
arbitrarily close to the bound. It is based on syndrome coding using linear convolutional codes with the optimal
binary quantizer implemented using the Viterbi algorithm run in the dual domain. The complexity and memory
requirements of the embedding algorithm are linear w.r.t. the number of cover elements. For practitioners,
we include detailed algorithms for finding good codes and their implementation. Finally, we report extensive
experimental results for a large set of relative payloads and for different distortion profiles, including the wet
paper channel.